Exercise 1_4_1: Basic Raster Operations

1

Create a matrix with 8 rows and 8 columns filled with random numbers in a range between 1 and 1000.

Clues

You can use the sample() function to create the numbers.

solution

library(dplyr)

fancy_matrix <-
  sample(1:1000, 8*8, replace = TRUE) |> 
  matrix(nrow = 8, ncol = 8)

fancy_matrix

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,]  115  428  558  747  643   40  569  380
## [2,]  103  850  731   99  898  601  763  666
## [3,]  874   92  102  982   12  669  617  797
## [4,]  156  436  558  128  364  467  627  469
## [5,]  994  160   93  506  579  134  425  903
## [6,]  352  264  918   70  528  535  418  798
## [7,]  976  388  413  283  589  793  542  455
## [8,]  805   14  622  833  355  211  700  889

2

Now use this matrix to create a raster layer and plot it.

Clues

The terra::rast() function can be fed with matrices to create a raster layer.

solution

library(terra)

fancy_raster_layer <- terra::rast(fancy_matrix)

terra::plot(fancy_raster_layer)

The terra::rast() function can not only be used to create raster data on the fly, which is also not very interesting. Instead, we can use it to import already prepared data.

3

Import one of the raster .tiff files in the ./data folder of the workshop directory.

Clues

Make sure your file paths are set correctly. You can check them with getwd(). Setting is done with setwd().

solution

immigrants_cologne <- terra::rast("./data/immigrants_cologne.tif")

4

Import the data on immigrants and inhabitants. Create a new layer showing the proportion of immigrants in each grid cell between 0 and 1. Next, apply z-standardization and dichotomize the data to get information about which grid cells deviate from the overall mean. Plot both “versions” of the data to see how they compare.

Clues

You can handle raster layers as any simple data table using +, -, or / operators. Z-standardization can be applied using the terra::scale() function.

solution

# load all layers
immigrants_cologne <-
  terra::rast("./data/immigrants_cologne.tif")

inhabitants_cologne <-
  terra::rast("./data/inhabitants_cologne.tif")

# create proportation layer
immigrants_proportion <- immigrants_cologne / inhabitants_cologne

# scale data
immigrants_proportion_scaled <- terra::scale(immigrants_proportation)

immigrants_proportion_scaled[immigrants_proportion_scaled < 0] <- 0
immigrants_proportion_scaled[immigrants_proportion_scaled > 0] <- 1

library(tmap)

tm_shape(immigrants_proportion) +
  tm_raster(palette = "-viridis")

tm_shape(immigrants_proportion_scaled) +
  tm_raster(palette = "-viridis")